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Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

269
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
269
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

162
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
162
Biological Methods for Microbial Control01:28

Biological Methods for Microbial Control

199
Biological agents offer an effective means of controlling microbial growth by leveraging natural processes like predation, competition, and the secretion of antimicrobial substances.Predatory bacteria such as Bdellovibrio species target and kill pathogens like Salmonella and E. coli. They are widely used in poultry farms to control infections. Myxococcus species help combat plant-pathogenic fungi. These naturally occurring predators serve as eco-friendly alternatives to chemical pesticides and...
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Related Experiment Video

Updated: Sep 9, 2025

Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

30.8K

Nanopore- and AI-empowered microbial viability inference.

Harika Ürel1,2,3,4, Sabrina Benassou5, Hanna Marti6

  • 1Helmholtz AI, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany.

Gigascience
|September 3, 2025
PubMed
Summary

This study introduces a computational framework using nanopore sequencing and AI to determine microbial viability from DNA signals. This method offers a sensitive and accurate alternative to traditional genomic approaches for various applications.

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Last Updated: Sep 9, 2025

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Assessing microbial viability is critical for ecological and clinical microbiome studies.
  • Current genomic methods for viability assessment are often labor-intensive, biased, and lack sensitivity.

Purpose of the Study:

  • To develop a novel computational framework for assessing microbial viability using nanopore sequencing data.
  • To leverage deep neural networks and explainable AI for accurate viability predictions.

Main Methods:

  • Utilized nanopore sequencing technology to capture raw signal data from microorganisms.
  • Developed deep neural networks to identify viability-specific patterns in nanopore signals.
  • Applied explainable AI to interpret model predictions and identify key signal features.

Main Results:

  • Achieved high accuracy in distinguishing viable from dead microorganisms in controlled experiments.
  • Demonstrated successful application in estimating viability of *Chlamydia* species, overcoming limitations of culture-based methods.
  • Showcased that the model can predict viability across different taxonomic groups.

Conclusions:

  • Presents the first computational framework for inferring microbial viability directly from nanopore signal data.
  • Highlights the potential for broad applications in environmental, veterinary, and clinical settings.
  • Acknowledges the need for further assessment of the framework's generalizability in metagenomic studies.